PROJECT SUMMARY/ABSTRACT Chronic tic disorders (referred to here as Tourette syndrome: TS) are complex and often serious neurodevelopmental disorders characterized by motor and/or vocal tics. Tics are brief, repetitive, unwanted movements or noises, which can severely impinge upon quality of life. While TS was once thought to be relatively rare, recent epidemiological studies find that 1-6% of all children meet criteria for a chronic tic disorder, making it a significant public health problem. Typically in TS, tics begin around age 5-7 years old, peak in severity around age 10-12 years, and improve throughout adolescence into adulthood. However, not all patients show this improvement during adolescence, as ~30% continue to experience significant impairment into adulthood. Thus, the years during and immediately following peak symptom severity represent a critical time for TS, during which individuals may show considerable improvement or not. Surprisingly little research has targeted this critical developmental stage of TS. Moreover, longitudinal investigations of predictors of TS outcome have focused primarily on single variables (e.g., caudate nucleus volume or tic severity). Yet there is considerable evidence that the neurobiology of TS is quite complex, involving interactions within and between multiple brain networks. For example, our preliminary findings demonstrate stronger brain functional connectivity among cognitive control networks and motor networks, as well as altered white and gray matter volumes in prefrontal and subcortical regions in TS. Using this complex information may be more informative for understanding tic severity changes and predicting clinical outcome. We propose a longitudinal study in which we will capture the developmental stage of TS with the greatest likelihood of change in tic severity (beginning at age 10-12 years), and will follow these children to track the development of brain and cognitive features, and how they relate to symptom change, over time. To capture the complex neurobiology of TS, we will collect whole-brain resting state functional connectivity, structural MRI, cognitive, and clinical data from a group of children with TS. We will compare these children to tic-free controls (from the NIH's ABCD Study Washington University site subject pool), as comparison to typical development will be essential for interpreting longitudinal changes in TS. We will target diagnostic differences and developmental changes in specific functional brain networks, regional brain volumes, and cognitive abilities. We will also use multivariate machine learning methods to unify this rich dataset to classify and make predictions about individual children. This approach analyzes complex patterns of multidimensional data rather than single variables, providing the potential for clinical utility and to contribute converging evidence about mechanism. Identifying mechanisms underlying symptom change will provide insight into why many children with TS improve while some do not, potentially yielding new targets for treatment and predictive indicators of persistent tics. Markers of symptom improvement could be targeted to treat children who do not improve. Being able to make predictions about individual children could identify those children who need those interventions most. We have expertise with every step of the proposed study, but the application to longitudinal data over the first half of the second decade of life is novel.